Podcasts about Particle

Play Episode Listen Later Mar 21, 2025 8:27

Check the full script on YouTube ⇒ https://youtu.be/H0SDpGKv22g⁠GENKI Japanese Textbook P195 (Edition 3) ▼Buy me a coffee and Be a sponsor of one episode ⁠⁠⁠⁠⁠⁠⁠⁠https://bit.ly/KANAKO-Coffee⁠⁠⁠⁠⁠⁠⁠⁠▼BGM ⇒ DOVA-SYNDROME（⁠⁠⁠⁠⁠⁠⁠⁠⁠https://dova-s.jp/⁠⁠⁠⁠⁠⁠⁠⁠⁠)▼Ending BGM ⇒Flower Field【FLASH☆BEAT】

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How he finds $1M+ CPG startup ideas before anyone else using Ahrefs and Manus AI

Where It Happens

Play Episode Listen Later Mar 19, 2025 42:32

I'm joined by Ashwinn Krishnaswamy, an expert in launching and growing consumer brands, where we discuss how he evaluates market opportunities and creates unfair advantages. We discuss various tools for market research including Ahrefs for keyword analysis, Particl for competitor sales data, and AI tools for product design. Ashwinn emphasizes that entrepreneurs should work backward from distribution channels rather than focusing solely on product development.Timestamps:00:00 - Intro03:45 - Ahrefs tool for keyword research15:08 - Particl for analyzing competitor sales data18:52 - Why start a CPG brand21:05 - How to stand out in crowded categories24:57- Opportunities in Branding for Older Adults28:40 - Bootstrapping Your Product Idea33:56 - Researching Your Market and Competitors36:52 - Manus AI for automating market research39:58 - Distribution is everything.Key Points:• Ashwinn shares tools and strategies for evaluating market opportunities for physical products and brands• Tools like Ahrefs and Particle provide valuable data on search volume, competition, and sales trends• AI tools like Manis can now automate much of the market research process that previously cost thousands• Distribution strategy should come before product development, not vice versa1) First, understand that DISTRIBUTION is everything."First-time founders focus on product, second-time founders focus on distribution."The hard truth? Many subpar products CRUSH IT because they nail distribution and operational excellence.Always work BACKWARDS from how you'll acquire customers!2) Use Ahrefs to evaluate category demand and competition Type any keyword to see:• Monthly search volume (market size)• Keyword difficulty (competition level)• Seasonal trends• Geographic distributionThis helps you VALIDATE demand before building anything!3) Look for geographic OPPORTUNITIES in the data When Ashwinn checked "electrolytes" he found:• 74% of searches from English-speaking Western countries• Only 3% from India, PhilippinesThis reveals potential to build "Element for Germany" or other untapped markets where trends haven't diffused yet!4) For physical products, use Particle to spy on competitorsThis tool shows:• E-commerce sales data for brands• Best-selling SKUs• Category trendsCrucial insight: Often 90% of revenue comes from just 4 SKUs!5) The REAL opportunity in physical products?They force you to become EXCEPTIONAL at marketing."The bleeding edge of marketing happens in consumer products because it's SO HARD to get attention and convince someone to part with their dollars."It's a marketing masterclass in real-time.6) How to stand out in crowded categories:Study the competition obsessively! Ashwinn showed how most magnesium supplements look generic and medical.But brands like Moon Juice and Lemme differentiate through:• Distinctive packaging• Custom bottles• Clear positioning• Targeting specific demographics7) The BIGGEST opportunity right now?Find UNDERSERVED demographics!Most brands target coastal millennials or Gen Z because that's who creates them.But what about products for older adults with:• More disposable income• Less saturated marketing channels• Different aesthetic preferences8) AI is your UNFAIR ADVANTAGE for research• Competitor analysis• Market sizing• Customer needs• Design trends"It's putting together a report Nielsen would charge $20K for!"99% of people aren't using AI this way yet.9) Before building ANYTHING, become a "relentless researcher":• Study every competitor's positioning• Read all the 1-2 star reviews in your category• Visit stores and talk to owners• Contact potential customers directlyNotable Quotes:"First time founders focus on product, second time founders focus on distribution." - Ashwinn"Building a physical product business is very hard and by most people probably just shouldn't be done on a whim. But if you can do it in a somewhat low stakes way or low risk way... I think it is a phenomenal way to get really good at marketing." - AshwinnLCA helps Fortune 500s and fast-growing startups build their future - from Warner Music to Fortnite to Dropbox. We turn 'what if' into reality with AI, apps, and next-gen products https://latecheckout.agency/BoringAds — ads agency that will build you profitable ad campaigns http://boringads.com/BoringMarketing — SEO agency and tools to get your organic customers http://boringmarketing.com/Startup Empire — a membership for builders who want to build cash-flowing businesses https://www.startupempire.coFIND ME ON SOCIALX/Twitter: https://twitter.com/gregisenbergInstagram: https://instagram.com/gregisenberg/LinkedIn: https://www.linkedin.com/in/gisenberg/FIND ASHWINN ON SOCIALX/Twitter: https://x.com/ShwinnabegoInstagram: https://www.instagram.com/shwinnabegobrandBrand Brothers Podcast: https://open.spotify.com/show/2wgTQ7mXBngdiNzcJ2cUJZ

Mystery of the Particle That Holds the Universe Together

Bright Side

Play Episode Listen Later Mar 16, 2025 12:39

The Higgs Boson, also called the "God Particle," is like the secret glue holding the universe together! Scientists first theorized it decades ago, but it wasn't actually discovered until 2012 at the Large Hadron Collider. Without it, atoms wouldn't have mass, meaning everything—stars, planets, even you—might not exist the way we do now.

Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer's Solution to Fix It)

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Play Episode Listen Later Mar 12, 2025 7:23

Have you ever noticed that software developers are frequently more productive than data scientists? The reason has nothing to do with coding ability.Software developers have known for decades that the real key to productivity lies somewhere else.In this quick Value Boost episode, software developer turned CEO Ben Johnson joins Dr Genevieve Hayes to discuss the focus management techniques that transformed his 20-year development career – which you can use to transform your data science productivity right now.Get ready to discover:The Kanban and focus currency techniques that replace notification-driven chaos [02:09]A 90-day planning system that beats imposter syndrome and drives results [03:09]Why two-hour focus blocks outperform constant context switching [04:19]The habit tracking method that helps you consistently “win the day” [06:12]Guest BioBen Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects.LinksConnect with Ben on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It)

La Galeria Nocturna Podcast

Play Episode Listen Later Mar 12, 2025 7:23

Genevieve Hayes Consulting Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It) Have you ever noticed that software developers are frequently more productive than data scientists? The reason has nothing to do with coding ability.Software developers have known for decades that the real key to productivity lies somewhere else.In this quick Value Boost episode, software developer turned CEO Ben Johnson joins Dr Genevieve Hayes to discuss the focus management techniques that transformed his 20-year development career – which you can use to transform your data science productivity right now.Get ready to discover:The Kanban and focus currency techniques that replace notification-driven chaos [02:09]A 90-day planning system that beats imposter syndrome and drives results [03:09]Why two-hour focus blocks outperform constant context switching [04:19]The habit tracking method that helps you consistently “win the day” [06:12] Guest Bio Ben Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects. Links Connect with Ben on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello and welcome to your value boost from value driven data science. The podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and I’m here with Ben Johnson, CEO and founder of Particle 41 to turbocharge your data science career in less time than it takes to run a simple query.[00:00:29] In today’s episode, we’re going to be discussing techniques from software development that data scientists can use to increase their productivity and efficiency. Welcome back, Ben.[00:00:42] Ben Johnson: Hey, nice to be here.[00:00:44] Dr Genevieve Hayes: As long time listeners of this show are probably already aware, before becoming a data scientist, my background was as an actuary and statistician.[00:00:53] And then when I decided to make the move to data science, I did a master’s in computer science to upskill on machine learning and AI. And one of the things I loved most about my master’s was that my classmates were predominantly software developers and engineers. And I found that Just by being in the same classes as them and associating with them on the class online forums, I learned just as much, if not more, about what it takes to be an effective data scientist as I did from the lectures themselves.[00:01:32] And this is because the software engineers had a very different perspective on data problem solving from what I’d developed as a statistician and actuary. Ben, in addition to being a serial entrepreneur, you yourself are a software developer with over 20 years of experience. In that time, you must have come across a whole range of techniques for boosting your productivity and efficiency as a developer.[00:02:02] Are there any techniques among those that, you’re surprised, data scientists don’t also use?[00:02:09] Ben Johnson: It kind of swirls together. So focus is a currency as kind of the tagline here. So the book, the one thing has been really inspirational for me. And I’m a bullet journaler. And so I kind of take my 90 day goals and break them down into months and then the weeks, you know,[00:02:26] what’s the one thing or the finer sets of things? I find a lot of digital professionals, including data scientists are kind of multitasking and we’ve kind of even created This kind of interruption culture in the way that we work. So I find it interesting when data scientists don’t have like the Kanban board or the flow of work and they’re just kind of operating by slack messages and emails.[00:02:50] And I think then you have Low currency of focus like you’re poor in focus. And so the overarching thing here is to be rich in focus. And that means creating systems and work environment and a personal organization strategy. That makes you richer in focus.[00:03:07] Dr Genevieve Hayes: And how would you go about doing that?[00:03:09] Ben Johnson: So I think it starts with like some level of personal ceremony.[00:03:14] And some adherence to routine. So it may seem confining, but I actually find it gives me a lot of freedom. So spend a lot of time around the quarter. Thinking like, what do I want to accomplish in the next 90 days and documenting that and then breaking that out in a month and not just doing it professionally, but doing it personally as well.[00:03:34] So that then when I go to my week, I’ve kind of planned my week. I know what my focuses are for at least some of the time. I don’t like knock it all down in stone. I leave some flex time in there for. Emails and slack messages, but I definitely know what needs to be true by the end of the week for me to feel accomplished and confident.[00:03:57] And in the end, the biggest enemy is the imposter syndrome, right? So I have to have to put challenges in front of me that I’m accomplishing. Because the last thing I want anybody on my team to feel is that imposter syndrome. And the only way we were get through that is by. Proving to ourselves that we can accomplish the goals that we put in front of ourselves.[00:04:19] Dr Genevieve Hayes: What you’ve described there is very similar to the approach that I take in my work. I read Cal Newport’s deep work about, three years ago. Yeah, and one of the things I find, you know, as a data scientist, often I do have multiple projects on the go. But I try and work in deep work blocks, so I schedule three two hour blocks per day, and I actually have a kitchen timer, and for that two hour block, I will only work on one particular task, and even if I’m working on multiple topics within a day.[00:04:55] I try and only have one task per day, but just having those two hour focus blocks really helps me to accomplish a lot.[00:05:03] Ben Johnson: Yeah, I think so. And what you’re talking about there is this time compression and I think time compression is very, very powerful. And I would say most people don’t. Incorporate an element of time compression, like your timer is time compression and incorporate environment. We kind of used to be.[00:05:23] We planned the year and we give very little cadence to the quarter and the month. And then we kind of realized. You know, Q3 we’re falling behind and then that would make for these awful Q4 experiences, right? People working right up into the last day of the year kind of thing. I think we’re seeing that improve and I think time compression, EOS is really big on the quarterly planning, the monthly planning.[00:05:50] And then you mentioned like the Pomodoro technique. These things are getting really popular, but those things are awarded by an increase. Like when you’re rich in focus, those things happen, right? Or you do those things to become more rich in focus.[00:06:06] Dr Genevieve Hayes: And my experience is the days when I do manage to have those focus blocks, I’m happier at the end of the day.[00:06:12] Ben Johnson: Yep. Yeah, because you created a scoreboard and you won the day, right? You know, you won the day. Yeah. In my bullet journal, I have a habit tracker and I put so many habits on there that if I do about half of them, like I’m good, and that works for me, you know, kind of always be solving.[00:06:28] You know salespeople, they always be closing and I’m kind of like always be doing something to make my life better, even if it’s just like drinking water, right? Remembering to drink water that’s a thing on my tracker.[00:06:42] Dr Genevieve Hayes: And that’s a wrap for today’s Value Boost. But if you want more insights from Ben, you’re in luck. We’ve got a longer episode with Ben where we discuss strategies for accelerating your data science impact and results. And it’s packed with no nonsense advice for turning your data skills into serious clout, cash, and career freedom.[00:07:04] You can find it now, wherever you found this episode, or at your favorite podcast platform. Well, thank you for joining me again, Ben.[00:07:12] Ben Johnson: Oh, my pleasure.[00:07:14] Dr Genevieve Hayes: And for those in the audience, thanks for listening. I’m Dr. Genevieve Hayes, and this has been Value Driven Data Science. The post Episode 55: [Value Boost] Why Data Scientists are Focus-Poor (and the Software Developer’s Solution to Fix It) first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Gothural | Episodio 1 | Goddamn Particle

Play Episode Listen Later Mar 10, 2025 81:56

Gothural - Reacción #1: Goddamn Particle ¡Bienvenidos al primer episodio de nuestras sesiones de reacción en | Gothural | Aquí no solo escuchamos, sino que despedazamos los nuevos lanzamientos de nuestra escena nacional con los propios creadores en la mesa. En esta entrega, nos acompaña la brutal alineación de Goddamn Particle, quienes se atreven a sentarse junto a los camaradas de GothProds y Brutal Revista para someter su álbum homónimo a un escrutinio sin piedad. ¿Qué encontramos en este disco? Riffs técnicos que golpean como un tubo directo a la cabeza. Composiciones complejas que desafían la lógica y los sentidos. ⚡ Voces que oscilan entre la brutalidad y la atmósfera etérea. Producción quirúrgica que no deja espacio para el error. Pero, ¿será un material digno de reventarnos los tímpanos o hay elementos que podrían haber sido más pulidos? Lo analizamos, lo desarmamos y lo ponemos bajo la lupa en una charla sin filtros, con la banda cara a cara. Reacciones crudas, opiniones viscerales y mucha pasión por el metal progresivo. ¿Sobrevivirá Goddamn Particle a la disección de Gothural? Descúbrelo en este episodio. ¡Dale play y únete a la conversación! #gothural #goddamnparticle #metalmexicano #HeadbangingSinFronteras #GothProds #LaGaleríaNocturna #PodcastDeMusica #podcasting #metal #metalhead #music #fest #metalfestival #metalfest #concert #livemusic #legends #cd #vinyl #unboxing #vinyl #vinylart #vinylcollector #vinylcollection #vinilo #colorvinyl #vinylrecords #AlternativeMetal GothProds Links Apple Podcast — https://podcasts.apple.com/mx/podcast/goth-prods/id1606324255?l=en Amazon Music — https://music.amazon.com.mx/podcasts/d10f63b6-f4f3-4a91-b21d-d98c2b08ca01/goth-prods?ref=dm_sh_xBGgYoDaqnREmWm0IoJu5r4kd Audible — https://www.audible.com/pd?asin=B09Q9YY6PF&source_code=ASSORAP0511160006&share_location=podcast_show_detail Facebook — https://www.facebook.com/Goth-Prods-104237088306624/ Instagram — https://www.instagram.com/goth_prods/ TikTok - https://www.tiktok.com/@goth_prods

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Episode 54: The Hidden Productivity Killer Most Data Scientists Miss

Play Episode Listen Later Mar 5, 2025 23:29

Genevieve Hayes Consulting Episode 54: The Hidden Productivity Killer Most Data Scientists Miss Why do some data scientists produce results at a rate 10X that of their peers?Many data scientists believe that better technologies and faster tools are the key to accelerating their impact. But the highest-performing data scientists often succeed through a different approach entirely.In this episode, Ben Johnson joins Dr Genevieve Hayes to discuss how productivity acts as a hidden multiplier for data science careers, and shares proven strategies to dramatically accelerate your results.This episode reveals:Why lacking clear intention kills productivity — and how to ensure every analysis drives real decisions. [02:11]A powerful “storyboarding” framework for turning vague requests into actionable projects. [09:51]How to deliver results faster using modern data architectures and raw data analysis. [13:19]The game-changing mindset shift that transforms data scientists from order-takers into trusted strategic partners. [17:05] Guest Bio Ben Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects. Links Connect with Ben on LinkedIn Connect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE Read Full Transcript [00:00:00] Dr Genevieve Hayes: Hello and welcome to Value Driven Data Science, the podcast that helps data scientists transform their technical expertise into tangible business value, career autonomy, and financial reward. I’m Dr. Genevieve Hayes, and today I’m joined by Ben Johnson, CEO and founder of Particle 41, a development firm that helps businesses accelerate their application development, data science, and DevOps projects.[00:00:30] In this episode, we’ll discuss strategies for accelerating your data science impact and results without sacrificing technical robustness. So get ready to boost your impact. Earn what you’re worth and rewrite your career algorithm. Ben, welcome to the show.[00:00:48] Ben Johnson: Yeah, thank you for having me.[00:00:50] Dr Genevieve Hayes: One of the most common misconceptions I see about data scientists is the mistaken belief that their worth within a business is directly linked to the technical complexity of the solutions they can produce.[00:01:04] And to a certain extent, this is true. I mean, if you can’t program, fit a model, or perform even the most basic statistical analysis, realistically, your days as a data scientist are probably numbered. However, while technical skills are certainly necessary to land a data science job, The data scientists I see making the biggest impact are the ones who are not necessarily producing the most complex solutions, but who can produce solutions to the most pressing business problems in the shortest possible time.[00:01:41] So in that sense, productivity can be seen as a hidden multiplier for data science careers. Ben, as the founder of a company that helps businesses accelerate their data science initiatives, it’s unsurprising that one of your areas of interest is personal productivity. Based on your experience, What are some of the biggest productivity killers holding data scientists back?[00:02:11] Ben Johnson: I don’t know for others. I know for myself that what kills my productivity is not having an intention or a goal or a direct target that I’m trying to go for. So when we solve the science problems, we’re really trying to figure out, like, what is that hunt statement or that question that key answer you know, the question that will bring the answer.[00:02:33] And also, what is the right level of information that would handle that at the asker’s level? So the ask is coming from a context or a person. And so we can know a lot. If that person is a fellow data scientist, then obviously we want to give them data. We want to answer them with data. But if that’s a results oriented business leader, then we need to make sure that we’re giving them information.[00:02:57] And we. Are the managers of the data, but to answer your question, I think that the biggest killer to productivity is not being clear on what question are we trying to answer?[00:03:08] Dr Genevieve Hayes: That, resonates with my own experience. One of the things I encountered early in my data science career was well, to take a step back. I originally trained as an actuary and worked as an actuary, and I was used to the situation where your boss would effectively tell you what to do. So, go calculate, calculate.[00:03:28] premiums for a particular product. So when I moved into data science, I think I expected the same from my managers. And so I would ask my boss, okay, what do you want me to do? And his answer would be something like, Oh here’s some data, go do something with it. And you can probably imagine the sorts of solutions that we got myself and my team would come up with something that was a model that looks like a fun fit[00:03:59] and those solutions tended to go down like a lead balloon. And it was only after several failures along those lines that it occurred to me, , maybe we should look at these problems from a different, point of view and figure out what is it that the senior management actually want to do with this data before starting to build a particular model from it.[00:04:24] Ben Johnson: Yeah. What decision are you trying to make? Just kind of starting with like the end in mind or the result in mind, I find in any kind of digital execution there are people who speak results language and there are people who speak solutions language. And when we intermix those two conversations,[00:04:41] it’s frustrating, it’s frustrating for the solution people to be like, okay, great. When are you going to give it to me? And it’s frustrating for the business folks, like hey, when am I going to get that answer when we want to talk about the solution? So I found like bifurcating like, okay, let’s have a results or planning discussion separate from a solution and asking for that right to proceed.[00:05:02] In the way that we communicate is super helpful., what your share reminds me of is some of the playbooks that we have around data QA, because in those playbooks, we’re doing analysis just for analysis sake. I feel like we’re looking for the outliers.[00:05:18] Okay. So if we look at this metric, these are the outliers. And really what we’re doing is we’re going back to the, originators of the data and say, like, sanity, check this for us. We want to run through a whole set of sanity checks to make sure that the pipeline that we’re about to analyze makes sense.[00:05:34] Are there any other exterior references that we can compare this to? And I do know that the first time we were participating in this concept of data QA, not having that playbook Was a problem, right? Like, well, okay. Yeah, the data is there. It’s good. It’s coming in, but you know, to really grind on that and make sure that it was reflective of the real world was an important step.[00:05:57] Dr Genevieve Hayes: So QA, I take your meaning quality assurance here? Is that right?[00:06:02] Ben Johnson: Yes. That’s the acronym quality assurance, but testing and doing QA around your data pipelines.[00:06:09] Dr Genevieve Hayes: Okay, so I get it. So actually making sure the pipelines work. And if you don’t understand what is it that you’re looking for with regard to performance, then you can end up going off in the wrong direction. Is that correct?[00:06:23] Ben Johnson: So if you were analyzing sales data, you would want to make sure that your totals reflected the financial reports. You just want to make sure that what you’ve. Accumulated in your analysis environment is reflective of the real world. There’s nothing missing. It generally makes sense. We just haven’t introduced any problem in just the organizing and collection of the data.[00:06:45] Dr Genevieve Hayes: Yeah, yeah. From my background in the insurance industry, those were all the sorts of checks that we used to have to do with the data as well.[00:06:52] Ben Johnson: Well, and oftentimes the folks that are asking these hard questions, they’re not asking the questions because they have any idea how clean the data they’ve collected. They just think there might be a chance. It’s like the dumb and dumber, you know, okay, so we think we have a chance, you know anyways awful movie reference, but they think that there might be a possibility that the answer to all of their questions or this hard decision that they need to make regularly is somewhere in that pile of stuff.[00:07:21] What we call a QA analysis Also is checking the data’s integrity if it’s even capable to solve the problem. So I think that’s a great first step and that sometimes that’s just kind of analysis for analysis sake or feels that way.[00:07:37] Dr Genevieve Hayes: One of the things you’ve touched on several times is the idea of the results oriented people and the solutions oriented people and I take it with the solutions oriented people, you’re talking about people like the data scientists. When the data scientists are talking to those results oriented people, Is there a framework that they can follow for identifying what sorts of results those results oriented people are looking for?[00:08:08] Ben Johnson: It’s very similar in the way that you approach like a UI UX design. We’ve taken kind of a storyboard approach, storyboard approach to what they want to see. Like, okay, What is the question? What are you expecting the answer to be? Like, what do you think would happen?[00:08:25] And then what kind of decisions are you going to do as a result of that? And you had some of those things as well. But kind of storyboarded out what’s the journey that they’re going to take, even if it’s just a logical journey through this data to go affect some change.[00:08:41] Dr Genevieve Hayes: So do you actually map this out on a whiteboard or with post it notes or something? So literally building a storyboard?[00:08:48] Ben Johnson: Most of the time , it’s bullets. It’s more of like written requirements. But when we think of it, we think of it , in a storyboard and often it’ll turn into like a PowerPoint deck or something because we’re also helping them with their understanding of the funding of the data science project, like connecting ROI and what they’re trying to do.[00:09:10] So yeah. Yeah, our firm isn’t just staff augmentation. We want to take a larger holistic ownership approach of the mission that we’re being attached to. So this is critical to like, okay, well, we’re going to be in a data science project together. Let’s make sure that we know what we’re trying to accomplish and what it’s for.[00:09:29] Because, you know, if you’re working on a complex project and six months in everybody forgets Why they’ve done this, like why they’re spending this money oftentimes you need to remind them and, show them where you are in the roadmap to solving those problems.[00:09:44] Dr Genevieve Hayes: With the storyboard approach, can you give me an example of that? Cause I’m still having a bit of trouble visualizing it.[00:09:51] Ben Johnson: Yeah, it’s really just a set of questions. What are you trying to accomplish? What do you expect to have happen? Where are you getting this data? It’s , just a discovery survey that we are thinking about when we’re establishing the ground rules of the particular initiative.[00:10:08] Dr Genevieve Hayes: And how do you go from that storyboard to the solution?[00:10:12] Ben Johnson: That’s a great question. So the solution will end up resolving in whatever kind of framework we’re using data bricks or whatever it’ll talk about the collection, the organization and the analysis. So we’ll break down how are we going to get this data is the data already in a place where we can start messing with it.[00:10:32] What we’re seeing is that a lot of. And I kind of going deep on the collection piece because that’s I feel like that’s like 60 percent of the work. We prefer a kind of a lake house type of environment where we’ll just leave a good portion of the data in its raw original format, analyze it.[00:10:52] Bring it into the analysis. And then, of course, we’re usually comparing that to some relational data. But all that collection, making sure we have access to all of that. And it’s in a in a methodology and pipelines that we can start to analyze it is kind of the critical first step. So we want to get our hands around that.[00:11:10] And then the organization. So is there, you know, anything we need to organize or is a little bit messy? And then what are those analysis? Like, what are those reports that are going to be needed or the visibility, the visualizations that would then be needed on top of that? And then what kind of decisions are trying to be made?[00:11:28] So that’s where the ML and the predictive analytics could come in to try to help assist with the decisions. And we find that most data projects. Follow those, centralized steps that we need to have answers for those.[00:11:43] Dr Genevieve Hayes: So a question that might need to be answered is, how much inventory should we have in a particular shop at a particular time? So that you can satisfy Christmas demand. And then you’d go and get the data about[00:11:59] Ben Johnson: Yeah. The purchase orders or yeah. Where’s the data for your purchase orders? Do you need to collect that from all your stores or do you already have that sitting in some place? Oh, yeah. It’s in all these, you know, disparate CSVs all over the place. We just did a. project for a leading hearing aid manufacturer.[00:12:18] And most of the data that they wanted to use was on a PC in the clinics. So we had to devise a collection mechanism in the software that the clinics were using to go collect all that and regularly import that into a place where We could analyze it, see if it was standardized enough to go into a warehouse or a lake.[00:12:39] And there were a lot of standardization problems, oddly, some of the clinics had kind of taken matters into their own hands and started to add custom fields and whatnot. So to rationalize all of that. So collection, I feel like is a 60 percent of the problem.[00:12:54] Dr Genevieve Hayes: So, we’ve got a framework for increasing productivity by identifying the right problem to solve, but the other half of this equation is how do you actually deliver results in a rapid fashion. because, as you know, A result today is worth far more than a result next year. What’s your advice around getting to those final results faster?[00:13:19] Ben Johnson: So That’s why I like the lake house architecture. We’re also finding new mechanisms and methodology. Some, I can’t talk about where they’re rather than taking this time to take some of the raw data and kind of continuously summarize it. So maybe you’re summarizing it and data warehousing it, but we like the raw data to stay there and just ask it the questions, but it takes more time and more processing power.[00:13:47] So what I’m seeing is we’re often taking that and organizing it into like a vector database or something that’s kind of right for the analysis. We’re also using vector databases in conjunction with AI solutions. So we’re asking the, we’re putting, we’re designing the vector database around the taxonomy, assuming that the user queries are going to match up with that taxonomy, and then using the LLM to help us make queries out of the vector database, and then passing that back to the LLM to test.[00:14:15] Talk about it to make rational sense about the story that’s being told from the data. So one way that we’re accelerating the answer is just to ask questions of the raw data and pay for the processing cost. That’s fast, and that also allows us to say, okay, do we have it?[00:14:32] Like, are we getting closer to having something that looks like the answer to your question? So we can be iterative that way, but at some point we’re starting to get some wins. In that process. And now we need to make those things more performant. And I think there’s a lot of innovation still happening in the middle of the problem.[00:14:51] Dr Genevieve Hayes: Okay, so you’re starting by questioning the raw data. Once you realize that you’re asking the right question and getting something that the results oriented people are looking for, would you then productionize this and start creating pipelines and asking questions of process data? Yeah.[00:15:11] Ben Johnson: Yeah. And we’d start figuring out how to summarize it so that the end user wasn’t waiting forever for an answer.[00:15:17] Dr Genevieve Hayes: Okay, so by starting with the raw data, you’re getting them answers sooner, but then you can make it more robust.[00:15:26] Ben Johnson: That’s right. Yes. More robust. More performant and then, of course, you could then have a wider group of users on the other side consuming that it wouldn’t just be a spreadsheet. It would be a working tool.[00:15:37] Dr Genevieve Hayes: Yeah, it’s one of the things that I was thinking about. I used to have a boss who would always say fast, cheap and good, pick two. Meaning that, you can have a solution now and it can be cheap, but it’s going to come at the cost of And it sounds like you focus on Fast and cheap first, with some sacrifice of quality because you are dealing with raw data.[00:16:00] But then, once you’ve got something locked in, you improve the quality of it, so then technical robustness doesn’t take a hit.[00:16:09] Ben Johnson: Yeah, for sure. I would actually say in the early stage, you’re probably sacrificing the cheap for good and fast because you’re trying to get data right off the logs, right off your raw data, whatever it is. And to get an answer really quickly on that without having to set up a whole lot of pipeline is fast.[00:16:28] And it’s it can be very good. It can be very powerful. We’ve seen many times where it like answers the question. You know, the question of, is that data worth? Mining further and summarizing and keeping around for a long time. So in that way, I think we addressed the ROI of it on the failures, right.[00:16:46] Being able to fail faster. Oh yeah. That data is not going to answer the question that we have. So we don’t waste all the time of what it would have been to process that.[00:16:55] Dr Genevieve Hayes: And what’s been the impact of taking this approach for the businesses and for the data scientists within your organisation who are taking this approach?[00:17:05] Ben Johnson: I think it’s the feeling of like. of partnership with us around their data where we’re taking ownership of the question and they’re giving us access to whatever they have. And there’s a feeling of partnership and the kind of like immediate value. So we’re just as curious about their business as they are.[00:17:27] And then we’re working shoulder to shoulder to help them determine the best way to answer those questions.[00:17:32] Dr Genevieve Hayes: And what’s been the change in those businesses between, before you came on board and after you came on board?[00:17:39] Ben Johnson: Well, I appreciate that question. So with many of the clients, they see that, oh, this is the value of the data. It has unlocked this realization that I, in the case of the hearing aid manufacturer that we work with, they really started finding that they could convert more clients and have a better brand relationship by having a better understanding of their data.[00:18:03] And they were really happy that they kept it. You know, 10 years worth of hearing test data around to be able to understand, their audience better and then turn that into. So they’ve seen a tremendous growth in brand awareness and that’s resulted in making a significant dent in maintaining and continuing to grow their market share.[00:18:26] Dr Genevieve Hayes: So they actually realize the true value of their data.[00:18:30] Ben Johnson: That’s right. And then they saw when they would take action on their data they were able to increase market share because they were able to affect people that truly needed to know about their brand. And like we’re seeing after a couple of years, their brand is like, you don’t think hearing aids unless you think of this brand.[00:18:48] So it’s really cool that they’ve been able to turn that data by really, Talking to the right people and sending their brand message to the right people.[00:18:56] Dr Genevieve Hayes: Yeah, because what this made me think of was one of the things I kept encountering in the early days of data science was a lot of Senior decision makers would bring in data scientists and see data science as a magic bullet. And then because the data scientists didn’t know what questions to answer, they would not be able to create the value that had been promised in the organization.[00:19:25] And the consequence after a year or two of this would be the senior decision makers would come to the conclusion that data science is just a scam. But it seems like by doing it right, you’ve managed to demonstrate to organizations such as this hearing aid manufacturer, that data science isn’t a scam and it can actually create value.[00:19:48] Ben Johnson: Absolutely. I see data sciences anytime that that loop works, right? Where you have questions. So even I have a small client, small business, he owns a glass manufacturing shop. And. The software vendor he uses doesn’t give him a inexpensive way to mark refer like who his salespeople are,[00:20:09] so he needs a kind of a salesperson dashboard. What’s really cool is that his software gives them, they get full access to a read only database. So putting a dashboard on top of. His data to answer this salesperson activities and commissions and just something like that. That’s data science.[00:20:28] And now he can monitor his business. He’s able to scale using his data. He’s able to make decisions on how many salespeople should I hire, which ones are performing, which ones are not performing. How should I pay them? That’s a lot of value to us as data scientists. It just seems like we just put a dashboard together.[00:20:46] But for that business, that’s a significant capability that they wouldn’t have otherwise had.[00:20:52] Dr Genevieve Hayes: So with all that in mind, what is the single most important change our listeners could make tomorrow? to accelerate their data science impact and results.[00:21:02] Ben Johnson: I would just say, be asking that question, Like what question am I trying to answer? What do you expect the outcome to be? Or what do you think the outcome is going to be? So that I’m not biased by that, but I’m sanity checking around that. And then what decisions are you going to make as a result?[00:21:19] I think always having that like in the front of your mind would help you be more consultative and help you work according to an intention. And I think that’s super helpful. Like don’t let the client Or the customer in your case, whether that be an internal person give you that assignment, like, just tell me what’s there.[00:21:38] Right. I just want insights. I think the have to push our leaders to give us a little more than that.[00:21:46] Dr Genevieve Hayes: the way I look at it is, don’t treat your job as though you’re someone in a restaurant who’s just taking an order from someone.[00:21:53] Ben Johnson: Sure.[00:21:54] Dr Genevieve Hayes: Look at it as though you’re a doctor who’s diagnosing a problem.[00:21:58] Ben Johnson: Yeah. And the data scientists that I worked with that have that like in their DNA, like they just can’t move forward unless they understand why they’re doing what they’re doing have been really impactful. In the organization, they just ask great questions and they quickly become an essential part of the team.[00:22:14] Dr Genevieve Hayes: So for listeners who want to get in contact with you, Ben, or to learn more about Particle 41, what can they do?[00:22:21] Ben Johnson: Yeah, I’m on LinkedIn. In fact I love talking to people about data science and DevOps and software development. And so I have a book appointment link on my LinkedIn profile itself. So I’m really easy to get into a call with, and we can discuss whatever is on your mind. I also offer fractional CTO services.[00:22:42] And I would love to help you with a digital problem.[00:22:45] Dr Genevieve Hayes: And there you have it. Another value packed episode to help turn your data science skills into serious clout, cash, and career freedom. If you enjoyed this episode, why not make it a double? Next week, catch Ben’s value boost, a quick five minute episode where he shares one powerful tip for getting real results real fast.[00:23:10] Make sure you’re subscribed so you don’t miss it. Thanks for joining me today, Ben.[00:23:16] Ben Johnson: Thank you. It was great being here. I enjoyed it[00:23:19] Dr Genevieve Hayes: And for those in the audience, thank you for listening. I’m Dr. Genevieve Hayes, and this has been value driven data science. The post Episode 54: The Hidden Productivity Killer Most Data Scientists Miss first appeared on Genevieve Hayes Consulting and is written by Dr Genevieve Hayes.

Episode 54: The Hidden Productivity Killer Most Data Scientists Miss

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Play Episode Listen Later Mar 5, 2025 23:29

Why do some data scientists produce results at a rate 10X that of their peers?Many data scientists believe that better technologies and faster tools are the key to accelerating their impact. But the highest-performing data scientists often succeed through a different approach entirely.In this episode, Ben Johnson joins Dr Genevieve Hayes to discuss how productivity acts as a hidden multiplier for data science careers, and shares proven strategies to dramatically accelerate your results.This episode reveals:Why lacking clear intention kills productivity — and how to ensure every analysis drives real decisions. [02:11]A powerful “storyboarding” framework for turning vague requests into actionable projects. [09:51]How to deliver results faster using modern data architectures and raw data analysis. [13:19]The game-changing mindset shift that transforms data scientists from order-takers into trusted strategic partners. [17:05]Guest BioBen Johnson is the CEO and Founder of Particle 41, a development firm that helps businesses accelerate their application development, data science and DevOps projects.LinksConnect with Ben on LinkedInConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

79. The Carp Fishing Podcast - Dan Kilgour - Particle Prep & Passion

Carp fishing podcast

Play Episode Listen Later Mar 4, 2025 185:24

We kick off this month's podcast with a deep dive into Mark's measurements, make what you will of that but be warned it caught me off guard. Topics of discussion this time round also include a deeper dive into the functionality of a carps gut governed by water temperatures. Do you think about the carp's cold water digestion process when you apply bait, maybe it's not front of mind but considering a carp's gut operates differently in cold water it's a topic that's worth exploring. We also have the second installment of Dan Kilgour's story. The first episode went down a storm, Dan's passion was infectious and the drive and determination he puts into his fishing, his particles and his profession was incredible. I really enjoyed recording these two episodes and I hope you get a real insight into the life of a working man, one juggling family life and carp fishing while growing his side hustle, the highly regarded Big Dogs Nuts. Enjoy.

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#22 Japanese Shadowing "Particle も" | 日本語でシャドーイング助詞の「も」

Play Episode Listen Later Mar 4, 2025 8:43

Check the full script on YouTube ⇒ https://youtu.be/pfOA9uuFPZsGENKI Japanese Textbook P111 (Edition 3) ▼Buy me a coffee and Be a sponsor of one episode https://bit.ly/KANAKO-Coffee▼BGM ⇒ DOVA-SYNDROME（⁠https://dova-s.jp/⁠)▼Ending BGM ⇒Flower Field【FLASH☆BEAT】

#13 Japanese Shadowing "Particleを" | 日本語でシャドーイング助詞の「を」

Play Episode Listen Later Feb 27, 2025 8:02

Check the full script on YouTube ⇒ ⁠https://youtu.be/knIoAWBYDdE⁠GENKI Japanese Textbook P88－89 (Edition 3) ▼Buy me a coffee and Be a sponsor of one episode ⁠https://bit.ly/KANAKO-Coffee⁠▼BGM ⇒ DOVA-SYNDROME（⁠⁠https://dova-s.jp/⁠⁠)▼Ending BGM ⇒Flower Field【FLASH☆B

#14 Japanese Shadowing "Particleで" | 日本語でシャドーイング助詞の「で」

Play Episode Listen Later Feb 27, 2025 8:02

Check the full script on YouTube ⇒ ⁠⁠https://youtu.be/_ZctT9YUU_8⁠GENKI Japanese Textbook P89 (Edition 3) ▼Buy me a coffee and Be a sponsor of one episode ⁠⁠https://bit.ly/KANAKO-Coffee⁠⁠▼BGM ⇒ DOVA-SYNDROME（⁠⁠⁠https://dova-s.jp/⁠⁠⁠)▼Ending BGM ⇒Flower Field【FLASH☆BEAT】

Engineered-Mind Podcast | Engineering, AI & Neuroscience

Smoothed Particle Hydrodynamics in the Industry - Andreas Bauer | Podcast #146

Play Episode Listen Later Feb 26, 2025 28:41

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#8 Japanese Shadowing "Particleも" | 日本語でシャドーイング助詞の「も」

Play Episode Listen Later Feb 24, 2025 8:55

Check the full script on YouTube ⇒ https://youtu.be/OPIqw32Qyqw GENKI Japanese Textbook P62-63 (Edition 3) ▼Buy me a coffee and Be a sponsor of one episode https://bit.ly/KANAKO-Coffee▼BGM ⇒ DOVA-SYNDROME（⁠https://dova-s.jp/⁠)▼Ending BGM ⇒Flower Field【FLASH☆BEAT】

#5 Japanese Shadowing "Particle の" | 日本語でシャドーイング助詞の「の」

Play Episode Listen Later Feb 22, 2025 10:37

Check the full script on YouTube ⇒ https://youtu.be/-rnU_u9kxncGENKI Japanese Textbook P43 (Edition 3) ▼Buy me a coffee and Be a sponsor of one episode https://bit.ly/KANAKO-Coffee▼BGM ⇒ DOVA-SYNDROME（⁠https://dova-s.jp/⁠)▼Ending BGM ⇒Flower Field【FLASH☆BEAT】

Lehto Files - Investigating UAPs

Science Fraud is Rampant - It also affects UAPs | Sabine Hossenfelder's Bombshell

Play Episode Listen Later Feb 19, 2025 15:18

Mainstream science has been lying to us. From high-energy physics to UAPs, the truth is being suppressed to protect careers, funding, and power. Sabine Hossenfelder exposed fraud in physics, where experiments are designed for funding, not discovery. Meanwhile, whistleblowers speaking out about UAPs and government secrets are being silenced, and Area 51 workers are being denied cancer treatment to keep classified programs hidden. It's time to demand the truth.

Could This Particle 'Clean Up' A Cosmic Mystery?

Short Wave

Play Episode Listen Later Feb 17, 2025 14:17

Physics has a bit of a messy problem: There's matter missing in our universe. Something is there that we can't see but can detect! What could this mysterious substance be? A lot of astronomers are searching for the answer. And some, like theoretical particle physicist Chanda Prescod-Weinstein, think a hypothetical particle called the axion may make this problem a little ... tidier. That's right: hypothetical. Scientists have never seen one, and don't know if they exist. So today, we point our cosmic magnifying glasses towards the axion and ask how scientists could find one — and if it could be the neat solution physicists have been searching for. Help shape the future of Short Wave by taking our survey: npr.org/shortwavesurveyListen to every episode of Short Wave sponsor-free and support our work at NPR by signing up for Short Wave+ at plus.npr.org/shortwave.Learn more about sponsor message choices: podcastchoices.com/adchoicesNPR Privacy Policy

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UK-China talks, RFK Jr. confirmation and record-breaking ‘ghost particle'

Improve the News

Play Episode Listen Later Feb 14, 2025 30:13

China's foreign minister visits the UK for the first time in a decade, Robert F. Kennedy Jr. is confirmed as the US' health secretary, El Salvador ends public funding for political parties, around 75K US federal workers accept Trump's buyout program, Chevron announces plans to cut 20% of its workforce, the UK's economy expands by 0.1% in Q4 of last year, a car drives into a Munich crowd in a suspected attack, the weight-loss drug semaglutide shows promise in reducing alcohol consumption, Trump moves to roll back Biden-era appliance efficiency rules, and scientists detect a record-breaking high-energy “ghost particle.” Sources: www.verity.news

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A Space Science Roundup With Brian Cox

All Of It

Play Episode Listen Later Feb 11, 2025 24:11

Particle physicist and science communicator Brian Cox takes questions on the nature of the universe, and shares his insights on the study of the sun, the upcoming 'planetary parade,' and the 100-year anniversary of Edwin Hubble's confirmation of galaxies beyond the Milky Way.

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465. In every particle is God - Gems from the Past

Self Inquiry

Play Episode Listen Later Feb 3, 2025 56:55

12th May 2021 These are teachings and pointers from ongoing NDA(Non-duality awareness)/Advaitic Satsangs held at Bhagavan Ramana Maharshi Centre in Melbourne, Australia. Om Namo Bhagavate Sri Arunachala Ramanaya !

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The Very Big and the Very Small Part 1 - A Conversation with a Cosmologist and Particle Physicist

Good Heavens! The Human Side of Astronomy

Play Episode Listen Later Feb 1, 2025 44:43

As human beings we occupy a wondrously peculiar size in the cosmos. With our unique stature, we are able to see and comprehend scales of the universe at the inconceivably huge and the unimaginably small. We here at Good Heavens! believe God has arranged this peculiar state of affairs and wants us to know and delight in what He has made. The heavens are telling of His glory and we want to be a part in getting His word and the works of His hands out there for everyone to contemplate, consider, and enjoy. On this very special episode of Good Heavens! Dan had the remarkable opportunity this month (January) at the Defend 2025 apologetics conference at New Orleans Baptist Theological Seminary to have a fun and inspiring chat with not one, but two professionally and popularly published doctors of the cosmos (at the same time!), one who studies the subatomic scale and one who studies galaxies, some of the largest entities in the cosmos. Dr. Michael G. Strauss is a particle physicist at CERN and the University of Oklahoma and Dr. Luke Barnes is a researcher and lecturer of cosmology and astrophysics at Western Sydney University in Sydney, Australia. Both have published works at the professional and popular levels. It was a wonder and a privilege to get them both at the table together to hear their insights about the structure of the universe at the large and small scales and how they see God in all of it. Here on part one, we chat about the standard models of cosmology and particle physics, the wonder of stars, and the unsolved mysteries facing doctors of the cosmos today. Whether you are a theist, non-theist, or take a young earth or old earth perspective as a Christian, we hope you'll find these episodes encouraging and informative. Above all, we hope it will inspire you to have thoughtful and respectful dialogue with others with whom you might disagree. Good Heavens! this was a fun conversation. We hope you'll enjoy it as much as Dan did putting it together for you! Soli Deo Gloria. More about Luke More about Michael Luke's books Michael's book Podbean enables our podcast to be on Apple Podcasts and other major podcast platforms. To support Good Heavens! on Podbean as a patron, you can use the Podbean app, or go to https://patron.podbean.com/goodheavens. This goes to Wayne Spencer. If you would like to give to the ministry of Watchman Fellowship or to Daniel Ray, you can donate at https://www.watchman.org/daniel. Donations to Watchman are tax deductible.

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The Very Big and the Very Small Part 2 - A Conversation with a Cosmologist and Particle Physicist

Good Heavens! The Human Side of Astronomy

Play Episode Listen Later Feb 1, 2025 36:42

How might things like "spooky action at a distance" or mysterious as "dark matter" point us to the glory of God? Whether you are a theist or a non-theist or a Christian who believes in a young or an old universe, we can all agree on at least one thing. The universe is a fascinating place in which we find ourselves. That the universe is intelligible to us is even more remarkable. But here at Good Heavens! we like to think the cosmos is more than just fascinating. We believe, as the Bible reveals, that the physical cosmos, at both the incredibly large and infinitesimally small scales, reveal to us something about God's invisible attributes. As both the psalms and Isaiah tell us, the whole of the heavens and earth are filled with the glory of God. Often we are told that Christianity and science cannot possibly go together, however. But hopefully our special guests will continue to dispel that misconception. As mentioned on part one, Dan had the remarkable opportunity this month (January) at the Defend 2025 apologetics conference at New Orleans Baptist Theological Seminary to have a fun and inspiring chat with not one, but two Christians who are professionally and popularly published doctors of the cosmos (at the same time!), one who studies the subatomic scale and one who studies galaxies, some of the largest entities in the cosmos. Dr. Michael G. Strauss is a particle physicist at CERN and the University of Oklahoma and Dr. Luke Barnes is a researcher and lecturer of cosmology and astrophysics at Western Sydney University in Sydney, Australia. Both have published works at the professional and popular levels. It was a wonder and a privilege to get them both at the table together to hear their insights about the structure of the universe at the large and small scales and how they see God in all of it. Here on part two, we chat about spooky action at a distance, the mystery of dark matter, and we'll glean some insights as to how we as Christians can have conversations about our differences in relation to the age of the universe. Good Heavens! this was a fun conversation. We hope you'll enjoy it as much as Dan did putting it together for you! Soli Deo Gloria. More about Luke More about Michael Luke's books Michael's book Podbean enables our podcast to be on Apple Podcasts and other major podcast platforms. To support Good Heavens! on Podbean as a patron, you can use the Podbean app, or go to https://patron.podbean.com/goodheavens. This goes to Wayne Spencer. If you would like to give to the ministry of Watchman Fellowship or to Daniel Ray, you can donate at https://www.watchman.org/daniel. Donations to Watchman are tax deductible.

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The OMG! Particle (Encore)

Everything Everywhere Daily History Podcast

Play Episode Listen Later Jan 30, 2025 15:32

On October 15, 1991, a cosmic ray detector in Utah observed something that had never been seen before or since. It was a cosmic ray with more energy than anything ever observed and more energy than most scientists thought possible. When one of the first researchers saw the data, they responded simply, “Oh, my God!” Learn more about the OMG particle, what it was, and what it means on this episode of Everything Everywhere Daily. Sponsors Mint Mobile Cut your wireless bill to 15 bucks a month at mintmobile.com/eed MasterClass Get up to 50% off at MASTERCLASS.COM/EVERYWHERE Quince Go to quince.com/daily for 365-day returns, plus free shipping on your order! ButcherBox New users that sign up for ButcherBox will receive 2 lbs of grass-fed ground beef in every box for the lifetime of their subscription + $20 off your first box when you use code daily at checkout! Subscribe to the podcast! https://everything-everywhere.com/everything-everywhere-daily-podcast/ -------------------------------- Executive Producer: Charles Daniel Associate Producers: Ben Long & Cameron Kieffer Become a supporter on Patreon: https://www.patreon.com/everythingeverywhere Update your podcast app at newpodcastapps.com Discord Server: https://discord.gg/UkRUJFh Instagram: https://www.instagram.com/everythingeverywhere/ Facebook Group: https://www.facebook.com/groups/everythingeverywheredaily Twitter: https://twitter.com/everywheretrip Website: https://everything-everywhere.com/ sd Learn more about your ad choices. Visit megaphone.fm/adchoices

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What Everyone Gets Wrong About AI -- and other science news of the week

Science with Sabine

Play Episode Listen Later Jan 28, 2025 26:06

This is our weekly compilation of science news 00:00 - There's a Third Type of Particle and We Never Knew 6:22 - What Everyone Gets Wrong About AI 13:43 - Fact Check: Did Physicists Really "Quash" the Multiverse Idea? 20:01 - A New Ice Age For Europe Is Becoming More Likely

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Heritage Events: The Power Hour | From the Particle to the Planet with Dr. Michael von Spakovsky

The Ricochet Audio Network Superfeed

Play Episode Listen Later Jan 27, 2025

The Power Hour is a weekly podcast that discusses the most interesting energy and environmental policy issues of the day with top national experts. Put your thinking cap on for this one, folks, and get ready to earn you PhD in thermodynamics! Jack invites Dr. Michael von Spakovsky to the Power Hour this week for […]

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Chinese Explained #63 - How to Use the Question Particle 吗 - Part 2

Play Episode Listen Later Jan 26, 2025 2:04

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Chinese Explained #62 - How to Use the Question Particle 吗 - Part 1

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Play Episode Listen Later Jan 26, 2025 4:12

learn how to use the question particle 吗

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The Power Hour | From the Particle to the Planet with Dr. Michael von Spakovsky

Heritage Events Podcast

Play Episode Listen Later Jan 25, 2025 62:33

The Power Hour is a weekly podcast that discusses the most interesting energy and environmental policy issues of the day with top national experts. Put your thinking cap on for this one, folks, and get ready to earn you PhD in thermodynamics! Jack invites Dr. Michael von Spakovsky to the Power Hour this week for a conversation that combines policy with technical, academic knowledge. Dr. von Spakovsky is a professor at Virginia Tech's Department of Mechanical Engineering who has spent a career thinking about how complex systems, large and small, work. It turns out that this research can help us to better understand how things like our climate and energy use work. Rather than framing his perspective around political preference or policy, he understands physics. This allows him to see through the political gas lighting and get straight to the facts. You might need listen to this one twice but it will be worth it. You find out more about Dr. von Spakovsky' s work here. Oh, and don't forget to grab your copy of Nuclear Revolution . Join the conversation at thepowerhour@heritage.org! Thank you for listening and please don't forget to subscribe and help us to spread the word. And buy both books!

Micah Nelson AKA Particle Kid, On Willie Nelson's Latest Recording Last Leaf On The Tree

Profiles With Maggie LePique

Play Episode Listen Later Jan 20, 2025 29:17

Maggie speaks with Willie Nelson's youngest son Micah Nelson also known as "Particle Kid" about his latest collaboration with his Father. Willie's 76th studio album and 153rd album overall as he celebrates his 91st year. Willie Nelson is now in his 7th decade as a songwriter, performer and recording artist, but one of his most underrated talents is song interpretation. Over his career he has tackled songs from across the world of music, making each his own. Willie turns his gaze to a collection of songs by rock legends, idiosyncratic singer songwriters, alt-rock heroes, and indie folk artists. The result is a gorgeously cohesive rumination on loss, love, and world-weary hope, perfectly complemented by spare and spooky musical production. Lovingly curated and produced by his son Micah Nelson, Last Leaf On The Tree finds Willie covering songs from moody indie rock (Beck), psych alt-pop (The Flaming Lips) and punk-informed folk (Sunny War, Micah's Particle Kid) to thought-provoking soul jazz (Nina Simone) and lesser-known gems from legends like Tom Waits, Neil Young, Keith Richards, and Warren Zevon. In addition, the album features new takes on one of Willie's oldest songs (“The Ghost” from 1962) plus a new one penned with Micah (“The Color Of Sound”) that joins Willie's collection of Zen-soaked classics. In addition to producing, Micah Nelson plays many of the instruments and even designed the album cover. He is joined by a host of celebrated musicians plus guest spots from legendary producer and musician Daniel Lanois, John Densmore of The Doors and harmonica master Mickey Raphael, who has played alongside Willie for over 50 years.Source: https://legacyrecordings.medium.com/willie-nelson-the-last-leaf-on-the-tree-d94188f65739Source: https://www.sonymusic.com/legacy/legacy-recordings-releases-willie-nelson-last-leaf-on-the-tree-track/Source: https://willienelson.com/Host Maggie LePique, a radio veteran since the 1980's at NPR in Kansas City Mo. She began her radio career in Los Angeles in the early 1990's and has worked for Pacifica station KPFK Radio in Los Angeles since 1994.Send us a textSupport the show@profileswithmaggielepique@maggielepique

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Wave or particle, your choice

High frequency tribe

Play Episode Listen Later Jan 17, 2025 82:28

Join and become a member of this channel, high frequency tribe homesteadhttps://www.youtube.com/channel/UCUMRaiiNMl3m7LxtCPI2tJg/joinJoin, become a member of the tinfoil brickapocalypse channel https://www.youtube.com/channel/UCLUuANxKvah7VDw0VbuRlyA/joinPURCHASE merchandise and ARThttps://www.etsy.com/shop/malibuillusionFinancially support this channelhttps://www.paypal.me/malibuillusionTikTok accounthttps://www.tiktok.com/@malibuillusion?_t=8rVj6gq6x9q&_r=1Podcast https://open.spotify.com/show/37O8Yj1y37Gr4VvExbmnaaInstagram https://www.instagram.com/sneaky_insects/profilecard/?igsh=MzRlODBiNWFlZA==Contact me via emailhamishpatterson69@yahoo.comBitcoin send to this address35uDZx9QNZiCbcrtxRMxo97E2Xvpecv9T7Etherium0xBB5190B3735F23329d7E7594C35c2AB9067eB7F2Bit chute channel https://www.bitchute.com/profile/CWxMDL2DOlCZNEW CHANNELSLEGO owl & illusion channelhttps://www.youtube.com/c/LEGOwithOwlIllusionLEGO Instagram https://www.instagram.com/brickshipearth/ Head in a Baghttps://www.youtube.com/channel/Contact me via emailhamishpatterson69@yahoo.com

head wave particle 1podcast

Beginning Japanese Phrases 190: ∼んです; ∼んだ [used when explaining something or giving new information; a casual form of のです/のだ; の (explanatory particle) relaxed intoん]

TheJapanesePage.com - Beginning Japanese Phrases

Play Episode Listen Later Jan 16, 2025 1:39

私(わたし)は東京(とうきょう)で生(う)まれたんです。 I | as for | Tokyo | in | was born I was born in Tokyo View this episode here: https://thejapanesepage.com/jphrases-ep-190 View all episodes here: https://thejapanesepage.com/beginning-japanese-phrases-podcast-and-videos/ -- Become a Makoto+ member and get show notes with complete vocabulary and grammar breakdowns as well as sound files of just the Japanese to download. www.MakotoPlus.com

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Chinese Explained #59 - How to Use the Linking Particle 是 - Part 2

Play Episode Listen Later Jan 12, 2025 1:33

review how to use the linking particle 是

culture travel chinese language linking particle chineseclass101

Chinese Explained #58 - How to Use the Linking Particle 是 - Part 1